Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation

• URL: http://arxiv.org/abs/2502.08685v1
• Date: Wed, 12 Feb 2025 12:01:08 GMT
• Title: Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
• Authors: Renqi Jia, Xiaokun Zhang, Bowei He, Qiannan Zhu, Weitao Xu, Jiehao Chen, Chen Ma,
• Abstract summary: Current methods employ data valuation to discern high-quality data from low-quality data. We propose an explainable and versatile framework DVR which can enhance the efficiency of data utilization tailored to any requirements. Our framework achieves up to 34.7% improvements over existing methods in terms of representative NDCG metric.
• Score: 10.964035199849125
• License:
• Abstract: User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods employ data valuation to discern high-quality data from low-quality data. However, they tend to employ black-box design, lacking transparency and interpretability. Besides, they are typically tailored to specific evaluation metrics, leading to limited generality across various tasks. To overcome these issues, we propose an explainable and versatile framework DVR which can enhance the efficiency of data utilization tailored to any requirements of the model architectures and evaluation metrics. For explainable data valuation, a data valuator is presented to evaluate the data quality via calculating its Shapley value from the game-theoretic perspective, ensuring robust mathematical properties and reliability. In order to accommodate various evaluation metrics, including differentiable and non-differentiable ones, a metric adapter is devised based on reinforcement learning, where a metric is treated as the reinforcement reward that guides model optimization. Extensive experiments conducted on various benchmarks verify that our framework can improve the performance of current recommendation algorithms on various metrics including ranking accuracy, diversity, and fairness. Specifically, our framework achieves up to 34.7\% improvements over existing methods in terms of representative NDCG metric. The code is available at https://github.com/renqii/DVR.

Related papers

• Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks [0.0]
  We show how statistical methodology can be used for quantifying uncertainty in metrics that have been aggregated across multiple tasks. These techniques reveal insights such as the dominance of a specific model for certain types of tasks despite an overall poor performance.
  arXiv  Detail & Related papers  (2025-01-08T02:17:34Z)
• Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning [71.2981957820888]
  We propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets. The framework initially generates diverse instruction data with multiple LLM agents through a bespoke sampling method. The generated data undergo a rigorous evaluation using a dual-model method that assesses both difficulty and quality.
  arXiv  Detail & Related papers  (2024-11-21T02:30:53Z)
• Is Reference Necessary in the Evaluation of NLG Systems? When and Where? [58.52957222172377]
  We show that reference-free metrics exhibit a higher correlation with human judgment and greater sensitivity to deficiencies in language quality. Our study can provide insight into the appropriate application of automatic metrics and the impact of metric choice on evaluation performance.
  arXiv  Detail & Related papers  (2024-03-21T10:31:11Z)
• A Novel Metric for Measuring Data Quality in Classification Applications (extended version) [0.0]
  We introduce and explain a novel metric to measure data quality. This metric is based on the correlated evolution between the classification performance and the deterioration of data. We provide an interpretation of each criterion and examples of assessment levels.
  arXiv  Detail & Related papers  (2023-12-13T11:20:09Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Exploring validation metrics for offline model-based optimisation with diffusion models [50.404829846182764]
  In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called the (ground truth) oracle. While an approximation to the ground oracle can be trained and used in place of it during model validation to measure the mean reward over generated candidates, the evaluation is approximate and vulnerable to adversarial examples. This is encapsulated under our proposed evaluation framework which is also designed to measure extrapolation.
  arXiv  Detail & Related papers  (2022-11-19T16:57:37Z)
• Data Optimisation for a Deep Learning Recommender System [0.0]
  This paper advocates privacy preserving requirements on collection of user data for recommender systems. First, we ask if restrictions on data collection will hurt test quality of RNN-based recommendations. Second, we ask if we can improve the quality under minimal data by using secondary data sources.
  arXiv  Detail & Related papers  (2021-06-21T16:05:37Z)
• MetricOpt: Learning to Optimize Black-Box Evaluation Metrics [21.608384691401238]
  We study the problem of optimizing arbitrary non-differentiable task evaluation metrics such as misclassification rate and recall. Our method, named MetricOpt, operates in a black-box setting where the computational details of the target metric are unknown. We achieve this by learning a differentiable value function, which maps compact task-specific model parameters to metric observations.
  arXiv  Detail & Related papers  (2021-04-21T16:50:01Z)
• GO FIGURE: A Meta Evaluation of Factuality in Summarization [131.1087461486504]
  We introduce GO FIGURE, a meta-evaluation framework for evaluating factuality evaluation metrics. Our benchmark analysis on ten factuality metrics reveals that our framework provides a robust and efficient evaluation. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated.
  arXiv  Detail & Related papers  (2020-10-24T08:30:20Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.